Malaria is a major health issue worldwide, and its diagnosis requires scalable solutions that can work effectively with low-cost microscopes (LCM). Deep learning-based methods have shown success in computer-aided diagnosis from microscopic images. However, these methods need annotated images that show cells affected by malaria parasites and their life stages. Annotating images from LCM significantly increases the burden on medical experts compared to annotating images from high-cost microscopes (HCM). For this reason, a practical solution would be trained on HCM images which should generalize well on LCM images during testing. While earlier methods adopted a multi-stage learning process, they did not offer an end-to-end approach. In this work, we present an end-to-end learning framework, named CodaMal (Contrastive Domain Adpation for Malaria). In order to bridge the gap between HCM (training) and LCM (testing), we propose a domain adaptive contrastive loss. It reduces the domain shift by promoting similarity between the representations of HCM and its corresponding LCM image, without imposing an additional annotation burden. In addition, the training objective includes object detection objectives with carefully designed augmentations, ensuring the accurate detection of malaria parasites. On the publicly available large-scale M5-dataset, our proposed method shows a significant improvement of 16% over the state-of-the-art methods in terms of the mean average precision metric (mAP), provides 21x speed up during inference, and requires only half learnable parameters than the prior methods. Our code is publicly available.

翻译：疟疾是全球性的重大健康问题，其诊断需要能够有效适用于低成本显微镜的可扩展解决方案。基于深度学习方法已在显微图像计算机辅助诊断中取得成效，但这些方法需要标注出受疟原虫感染细胞及其生命阶段的图像。与高成本显微镜图像标注相比，低成本显微镜图像标注会显著增加医学专家的工作负担。因此，可行的解决方案应基于高成本显微镜图像进行训练，并在测试阶段对低成本显微镜图像具备良好的泛化能力。早期方法虽采用多阶段学习流程，但未能实现端到端方案。本文提出名为CodaMal（对比域适应疟疾检测）的端到端学习框架。为弥合高成本显微镜（训练集）与低成本显微镜（测试集）之间的差异，我们提出域自适应对比损失函数，该函数通过增强高成本显微镜图像及其对应低成本显微镜图像表征的相似性来减少域偏移，且无需额外标注负担。训练目标同时包含目标检测目标函数与精心设计的增强策略，确保疟原虫检测准确性。在公开大规模M5数据集上，本方法较现有最优方法在平均精度指标上提升16%，推理速度提升21倍，且可学习参数仅为现有方法的一半。我们的代码已公开。